Network traffic monitoring based on CNN-SVM
In a modern complex network, network monitoring and measurement have become increasingly important. The traditional network traffic monitoring methods face the challenge of efficiency and accuracy when dealing with massive data. The proposed hybrid model in this study uses convolutional neural netwo...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01014.pdf |
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| _version_ | 1825206600112013312 |
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| author | Wu Qian |
| author_facet | Wu Qian |
| author_sort | Wu Qian |
| collection | DOAJ |
| description | In a modern complex network, network monitoring and measurement have become increasingly important. The traditional network traffic monitoring methods face the challenge of efficiency and accuracy when dealing with massive data. The proposed hybrid model in this study uses convolutional neural networks (CNNs) and support vector machines (SVMs) to address these concerns and increase the effectiveness of network traffic monitoring. This paper uses CNN to extract features from network traffic data. CNN has the ability to recognize intricate patterns in the data and automatically extract valuable characteristics from the raw data. The SVM classifier receives the retrieved characteristics and uses them to further classify the data in order to distinguish between normal and abnormal traffic. By doing this, this paper may more successfully combine the benefits of SVM for classification with CNN’s advantages for feature learning, enhancing traffic monitoring’s precision and resilience. According to the experimental data, the hybrid model performs far better in network traffic categorization tasks than the standard techniques, with a reduced false positive rate and higher accuracy. This research shows that CNN-SVM model is an effective network traffic monitoring tool, which can provide high quality detection results while ensuring high efficiency. |
| format | Article |
| id | doaj-art-dae45599cccb40cfba86a593dc634b0c |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-dae45599cccb40cfba86a593dc634b0c2025-02-07T08:21:12ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700101410.1051/itmconf/20257001014itmconf_dai2024_01014Network traffic monitoring based on CNN-SVMWu Qian0Department of Computer Science and Software Engineering, School of Hebei University of TechnologyIn a modern complex network, network monitoring and measurement have become increasingly important. The traditional network traffic monitoring methods face the challenge of efficiency and accuracy when dealing with massive data. The proposed hybrid model in this study uses convolutional neural networks (CNNs) and support vector machines (SVMs) to address these concerns and increase the effectiveness of network traffic monitoring. This paper uses CNN to extract features from network traffic data. CNN has the ability to recognize intricate patterns in the data and automatically extract valuable characteristics from the raw data. The SVM classifier receives the retrieved characteristics and uses them to further classify the data in order to distinguish between normal and abnormal traffic. By doing this, this paper may more successfully combine the benefits of SVM for classification with CNN’s advantages for feature learning, enhancing traffic monitoring’s precision and resilience. According to the experimental data, the hybrid model performs far better in network traffic categorization tasks than the standard techniques, with a reduced false positive rate and higher accuracy. This research shows that CNN-SVM model is an effective network traffic monitoring tool, which can provide high quality detection results while ensuring high efficiency.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01014.pdf |
| spellingShingle | Wu Qian Network traffic monitoring based on CNN-SVM ITM Web of Conferences |
| title | Network traffic monitoring based on CNN-SVM |
| title_full | Network traffic monitoring based on CNN-SVM |
| title_fullStr | Network traffic monitoring based on CNN-SVM |
| title_full_unstemmed | Network traffic monitoring based on CNN-SVM |
| title_short | Network traffic monitoring based on CNN-SVM |
| title_sort | network traffic monitoring based on cnn svm |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01014.pdf |
| work_keys_str_mv | AT wuqian networktrafficmonitoringbasedoncnnsvm |